Abstract : This analysis reviews the management of vulnerabilities and security risks of Internet of Things (IoT). This paper provides an overview, which it reveals the recent Internet's growth and how it has transformed our lives in various, unforeseen dimensions and how it has given rise to IoT. The introduction part focuses on providing an analysis on literature by presenting a short IoT history, some technical information on security protocols, and IoT hardware problems. The section on survey is where similar literatures on specific concepts are reviewed by describing the vulnerabilities and threats of IoT systems, and then reviewed risk management mechanisms for both information technologies and information protection. After the review, the analysis and discussion segment addressed and evaluated the details contained in the literature review. In this paper, a new risk management strategy uniquely designed for each IoT system is proposed. Then proposed work is evaluated by discussing the advantages and concluded the analysis and the future work.

Keywords: IoT, Security, Threats, Vulnerability, Risk Management Framework

Abstract : Cloud computing is a technique for storing the information virtually. It may comprise of database, storage, tools, servers, networking and software services. It deals with virtual storing and retrieving of data from anywhere by the help of internet. This paper uses cloud computing technique in education for uploading study materials, videos, sharing information to the students and for conducting tests. The symmetric key encryption technique is used in this concept, where one key is utilized for both decryption and encryption. The advanced encryption standard algorithm (AES) was used for securing the data in the cloud, where it gives high security and faster execution time. This technique is mainly based on improving the concept of virtual classroom by using cloud computing.

Keywords: Brain tumor, Computer-Aided Diagnosis, Deep Learning, Fusion Feature, Recurrent Extreme Learning Machine, Artificial Bee Colony.

Abstract : The security for cloud network systems is essential and significant to secure the data source from intruders and attacks. Implementing an intrusion detection system (IDS) for securing from those intruders and attacks is the best option. Many IDS models are presently based on different techniques and algorithms like machine learning and deep learning. In this research, IDS for the cloud computing environment is proposed. Here in this model, the genetic algorithm (GA) and back propagation neural network (BPNN) is used for attack detection and classification. The Canadian Institute for Cyber-security CIC-IDS 2017 dataset is used for the evaluation of performance analysis. Initially, from the dataset, the data are preprocessed, and by using the genetic algorithm, the attack was detected. The detected attacks are classified using the BPNN classifier for identifying the types of attacks. The performance analysis was executed, and the results are obtained and compared with the existing machine learning-based classifiers like FC-ANN, NB-RF, KDBN, and FCM-SVM techniques. The proposed GA-BPNN model outperforms all these classifying techniques in every performance metric, like accuracy, precision, recall, and detection rate. Overall, from the performance analysis, the best classification accuracy is achieved for Web attack detection with 97.90%, and the best detection rate is achieved for Brute force attack detection with 97.89%.

Keywords: Intrusion Detection, Cloud Computing, Genetic algorithm, Back Propagation Neural Network, CIC-IDS.

Abstract : The second most diagnosed disease of men throughout the world is Prostate cancer (PCa). 28% of cancers in men result in the prostate, making PCa and its identification an essential focus in cancer research. Hence, developing effective diagnostic methods for PCa is very significant and has critical medical effect. These methods could improve the advantages of treatment and enhance the patients' survival chance. Imaging plays a significant role in the identification of PCa. Prostate segmentation and classification is a difficult process, and the difficulties fundamentally vary with one imaging methodology then onto the next. For segmentation and classification, deep learning algorithms, specifically convolutional networks, have quickly become an optional technique for medical image analysis. In this survey, various types of imaging modalities utilized for diagnosing PCa is reviewed and researches made on the detection of PCa is analyzed. Most of the researches are done in machine learning based and deep learning based techniques. Based on the results obtained from the analysis of these researches, deep learning based techniques plays a significant and promising part in detecting PCa. Most of the techniques are based on computer aided detection (CAD) systems, which follows preprocessing, segmentation, feature extraction, and classification processes, which yield efficient results in detecting PCa. As a conclusion from the analysis of some recent works, deep learning based techniques are adequate for the detection of PCa.

Keywords:Prostate Cancer, CAD system, MRI, Ultrasound, Segmentation, Classification

Abstract : : Determining the size of the tumor is a significant obstacle in brain tumour preparation and objective assessment. Magnetic Resonance Imaging (MRI) is one of the non-invasive methods that has emanated without ionizing radiation as a front-line diagnostic method for brain tumour. Several approaches have been applied in modern years to segment MRI brain tumours automatically. These methods can be divided into two groups based on conventional learning, such as support vectormachine (SVM) and random forest, respectively hand-crafted features and classifier method. However, after deciding hand-crafted features, it uses manually separated features and is given to classifiers as input. These are the time consuming activity, and their output is heavily dependent upon the experience of the operator. This research proposes fully automated detection of brain tumor using Convolutional Neural Network (CNN) to avoid this problem. It also uses brain image of high grade gilomas from the BRATS 2015 database. The suggested research performs brain tumor segmentation using clustering of k-means and patient survival rates are increased with this proposed early diagnosis of brain tumour using CNN.

Keywords: Alzheimer's disease, Dementia, CNN, VGG-19, OASIS database, MRI.